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Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
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Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
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Journal Article
Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
2023
Overview
Real-time tool wear prediction and its remaining useful life (RUL) estimation is an important part of the development of a smart machining system while it is practically complex. A two-step framework proposed based on the statistical correlation of the experimentally measured cutting tool vibration data with the flank wear progression and estimation of the cutting tool RUL by the construction of stochastic tool life probability predictive models. The machining experiments are conducted on the IN718 superalloy with uncoated WC tools under the varied conditions of cutting speed and feed to acquire the data of flank wear and associated tool vibration data. The results of confirmation experiments show the statistical correlation constructed is practically viable for in-process flank wear prediction at any time of instance during machining with any set cutting conditions using the real-time tool vibration monitoring. The in-process observation of 1.5 g tool acceleration during machining with 60 m/min cutting speed and 0.1 mm/tooth feed signifies 15% of the cutting tool failure probability, and its remaining useful life is 12.91 min. For 50% of tool reliability machining with 0.1 mm/tooth feed and 60, 90 and 120 m/min cutting speed, tool accelerations of 2.01, 3.08 and 3.98 g reflect that the respective exhausted tool lives are 12, 8 and 6 min and the respective remaining useful lives are 8, 6 and 5 min. Hence, based on the presented analysis and results, it is envisaged the proposed framework is reliable and robust for in-process cutting tool condition prediction based on the real-time tool vibration monitoring for its adoption to develop a smart machining system with autonomous decision-making capability.
Publisher
Springer Paris,Springer Nature B.V
Subject
